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Information-based methods for neuroimaging: analyzing structure, function and dynamics

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889195022 Year: Pages: 191 DOI: 10.3389/978-2-88919-502-2 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2015-12-03 13:02:24
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The aim of this Research Topic is to discuss the state of the art on the use of Information-based methods in the analysis of neuroimaging data. Information-based methods, typically built as extensions of the Shannon Entropy, are at the basis of model-free approaches which, being based on probability distributions rather than on specific expectations, can account for all possible non-linearities present in the data in a model-independent fashion.Mutual Information-like methods can also be applied on interacting dynamical variables described by time-series, thus addressing the uncertainty reduction (or information) in one variable by conditioning on another set of variables.In the last years, different Information-based methods have been shown to be flexible and powerful tools to analyze neuroimaging data, with a wide range of different methodologies, including formulations-based on bivariate vs multivariate representations, frequency vs time domains, etc. Apart from methodological issues, the information bit as a common unit represents a convenient way to open the road for comparison and integration between different measurements of neuroimaging data in three complementary contexts: Structural Connectivity, Dynamical (Functional and Effective) Connectivity, and Modelling of brain activity. Applications are ubiquitous, starting from resting state in healthy subjects to modulations of consciousness and other aspects of pathophysiology.Mutual Information-based methods have provided new insights about common-principles in brain organization, showing the existence of an active default network when the brain is at rest. It is not clear, however, how this default network is generated, the different modules are intra-interacting, or disappearing in the presence of stimulation. Some of these open-questions at the functional level might find their mechanisms on their structural correlates. A key question is the link between structure and function and the use of structural priors for the understanding of the functional connectivity measures. As effective connectivity is concerned, recently a common framework has been proposed for Transfer Entropy and Granger Causality, a well-established methodology originally based on autoregressive models. This framework can open the way to new theories and applications.This Research Topic brings together contributions from researchers from different backgrounds which are either developing new approaches, or applying existing methodologies to new data, and we hope it will set the basis for discussing the development and validation of new Information-based methodologies for the understanding of brain structure, function, and dynamics.

Entropy Measures for Data Analysis: Theory, Algorithms and Applications

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ISBN: 9783039280322 9783039280339 Year: Pages: 260 DOI: 10.3390/books978-3-03928-033-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2020-01-07 09:21:22
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Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses. The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given.

Keywords

experiment of design --- empirical mode decomposition --- signal analysis --- similarity indices --- synchronization analysis --- auditory attention --- entropy measure --- linear discriminant analysis (LDA) --- support vector machine (SVM) --- auditory attention classifier --- electroencephalography (EEG) --- vague entropy --- distance induced vague entropy --- distance --- complex fuzzy set --- complex vague soft set --- entropy, entropy visualization --- entropy balance equation --- Shannon-type relations --- multivariate analysis --- machine learning evaluation --- data transformation --- sample entropy --- treadmill walking --- center of pressure displacement --- dual-tasking --- analog circuit --- fault diagnosis --- cross wavelet transform --- Tsallis entropy --- parametric t-distributed stochastic neighbor embedding --- support vector machine --- information transfer --- Chinese stock sectors --- effective transfer entropy --- market crash --- system coupling --- cross-visibility graphs --- image entropy --- geodesic distance --- Dempster-Shafer evidence theory --- uncertainty of basic probability assignment --- belief entropy --- plausibility transformation --- weighted Hartley entropy --- Shannon entropy --- learning --- information --- novelty detection --- non-probabilistic entropy --- learning systems --- permutation entropy --- embedded dimension --- short time records --- signal classification --- relevance analysis --- global optimization --- meta-heuristic --- firefly algorithm --- cross-entropy method --- co-evolution --- symbolic analysis --- ordinal patterns --- Permutation entropy --- conditional entropy of ordinal patterns --- Kolmogorov-Sinai entropy --- algorithmic complexity --- information entropy --- particle size distribution --- selfsimilar measure --- simulation --- data analysis --- entropy --- entropy measures --- automatic learning

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